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Changing Minds by Using Open Data

- June 26, 2018 in communication, Data, Featured, guestpost, oer

Guest post by Erdinç Saçan & Robert Schuwer Fontys University of Applied Sciences, the Netherlands

The Greek philosopher Pythagoras once said:

“if you want to multiply joy, then you have to share.”

This also applies to data. Who shares data, gets a multitude of joy – value – in return.

  ICT is not just about technology – it’s about coming up with solutions to solve problems or to help people, businesses, communities and governments. Developing ICT solutions means working with people to find a solution. Students in Information & Communication Technology learn how to work with databases, analysing data and making dashboards that will help the users to make the right decisions.  Data collections are required for these learning experiences. You can create these data collections (artificially) yourself or use “real” data collections, openly available (like those from Statistics Netherlands (CBS) (https://www.cbs.nl/en-gb)) In education, data is becoming increasingly important, both in policy, management and in the education process itself. The scientific research that supports education is becoming increasingly dependent on data. Data leads to insights that help improve the quality of education (Atenas & Havemann, 2015). But in the current era where a neo-liberal approach of education seems to dominate, the “Bildung” component of education is considered more important than ever. The term Bildung is attributed to Willem van Humboldt (1767-1835). It refers to general evolution of all human qualities, not only acquiring knowledge, but also developing skills for moral judgments and critical thinking.

Study

In (Atenas & Havemann, 2015), several case studies are described where the use of open data contributes to developing the Bildung component of education. To contribute to these cases and eventually extend experiences, a practical study has been conducted. The study had the following research question: “How can using open data in data analysis learning tasks contribute to the Bildung component of the ICT Bachelor Program of Fontys School of ICT in the Netherlands?” In the study, an in-depth case study is executed, using an A / B test method. One group of students had a data set with artificial data available, while the other group worked with a set of open data from the municipality of Utrecht. A pre-test and post-test should reveal whether a difference in development of the Bildung component can be measured. Both tests were conducted by a survey. Additionally, some interviews have been conducted afterwards to collect more in-depth information and explanations for the survey results. For our A/B test, we used three data files from the municipality of Utrecht (a town in the center of the Netherlands, with ~350,000 inhabitants). These were data from all quarters in Utrecht:
  • Crime figures
  • Income
  • Level of Education
(Source: https://utrecht.dataplatform.nl/data) We assumed, all students had opinions on correlations between these three types of data, e.g. “There is a proportional relation between crime figures and level of education” or “There is an inversely proportional relation between income and level of education”. We wanted to see which opinions students had before they started working with the data and if these opinions were influenced after they had analyzed the data. A group of 40 students went to work with the data. The group was divided into 20 students who went to work with real data and 20 went to work with ‘fake’ data. Students were emailed with the three data files and the following assignment: “check CSV (Excel) file in the attachment. Please try this to do an analysis. Try to draw a minimum of 1, a maximum of 2 conclusions from it… this can be anything. As long as it leads to a certain conclusion based on the figures.” In addition, there was also a survey in which we tried to find out how students currently think about correlations between crime, income and educational level. Additionally, some students were interviewed to get some insights into the figures collected by the survey.  

Results

For the survey, 40 students have been approached. The response consisted of 25 students. All students indicated that working with real data is more fun, challenging and concrete. It motivates them. Students who worked with fake data did not like this as much. In interviews they indicated that they prefer, for example, to work with cases from companies rather than cases invented by teachers. In the interviews, the majority of students indicated that by working with real data they have come to a different understanding of crime and the reasons for it. They became aware of the social impact of data and they were triggered to think about social problems. To illustrate, here some responses students gave in interviews “Before I started working with the data, I had always thought that there was more crime in districts with a low income and less crime in districts with a high income. After I have analyzed the data, I have seen that this is not immediately the case. So my thought about this has indeed changed. It is possible, but it does not necessarily have to be that way.” (M. K.) “At first, I also thought that there would be more crime in communities with more people with a lower level of education than in communities with more people with a higher level of education. In my opinion, this image has changed in part. I do not think that a high or low level of education is necessarily linked to this, but rather to the situation in which they find themselves. So if you are highly educated, but things are really not going well (no job, poor conditions at home), then the chance of criminality is greater than if someone with a low level of education has a job.” ( A. K.) “I think it has a lot of influence. You have an image and an opinion beforehand. But the real data either shows the opposite or not. And then you think, “Oh yes, this is it.’. And working with fake data, is not my thing. It has to provide real insights.” (M.D.)

Conclusion

Our experiment provided positive indications that contributing to the Bildung component of education by using open data in data analysis exercises is possible. Next steps to develop are both extending these experiences to larger groups of students and to more topics in the curriculum.  

References

Atenas, J. & Havemann, L. (2015). Open Data as Open Educational Resources: Towards Transversal Skills and Global Citizenship. Open praxis7(4), 377-389. http://dx.doi.org/10.5944/openpraxis.7.4.233 Atenas, J., & Havemann, L. (Eds.). (2015). Open Data as Open Educational Resources: Case studies of emerging practice. London: Open Knowledge, Open Education Working Group. https://education.okfn.org/handbooks/open-data-as-open-educational-resources/ 
About the authors  Erdinç Saçan is a Senior Teacher of ICT & Business and the Coordinator of the Minor Digital Marketing @ Fontys University of Applied Sciences, School of ICT in Eindhoven, the Netherlands. He previously worked at Corendon, TradeDoubler and Prijsvrij.nl       Robert Schuwer is Professor Open Educational Resources at Fontys University of Applied Sciences, School of ICT in Eindhoven, the Netherlands and  holds the UNESCO Chair on Open Educational Resources and Their Adoption by Teachers, Learners and Institutions.

Changing Minds by Using Open Data

- June 26, 2018 in communication, Data, Featured, guestpost, oer

Guest post by Erdinç Saçan & Robert Schuwer Fontys University of Applied Sciences, the Netherlands

The Greek philosopher Pythagoras once said:

“if you want to multiply joy, then you have to share.”

This also applies to data. Who shares data, gets a multitude of joy – value – in return.

  ICT is not just about technology – it’s about coming up with solutions to solve problems or to help people, businesses, communities and governments. Developing ICT solutions means working with people to find a solution. Students in Information & Communication Technology learn how to work with databases, analysing data and making dashboards that will help the users to make the right decisions.  Data collections are required for these learning experiences. You can create these data collections (artificially) yourself or use “real” data collections, openly available (like those from Statistics Netherlands (CBS) (https://www.cbs.nl/en-gb)) In education, data is becoming increasingly important, both in policy, management and in the education process itself. The scientific research that supports education is becoming increasingly dependent on data. Data leads to insights that help improve the quality of education (Atenas & Havemann, 2015). But in the current era where a neo-liberal approach of education seems to dominate, the “Bildung” component of education is considered more important than ever. The term Bildung is attributed to Willem van Humboldt (1767-1835). It refers to general evolution of all human qualities, not only acquiring knowledge, but also developing skills for moral judgments and critical thinking.

Study

In (Atenas & Havemann, 2015), several case studies are described where the use of open data contributes to developing the Bildung component of education. To contribute to these cases and eventually extend experiences, a practical study has been conducted. The study had the following research question: “How can using open data in data analysis learning tasks contribute to the Bildung component of the ICT Bachelor Program of Fontys School of ICT in the Netherlands?” In the study, an in-depth case study is executed, using an A / B test method. One group of students had a data set with artificial data available, while the other group worked with a set of open data from the municipality of Utrecht. A pre-test and post-test should reveal whether a difference in development of the Bildung component can be measured. Both tests were conducted by a survey. Additionally, some interviews have been conducted afterwards to collect more in-depth information and explanations for the survey results. For our A/B test, we used three data files from the municipality of Utrecht (a town in the center of the Netherlands, with ~350,000 inhabitants). These were data from all quarters in Utrecht:
  • Crime figures
  • Income
  • Level of Education
(Source: https://utrecht.dataplatform.nl/data) We assumed, all students had opinions on correlations between these three types of data, e.g. “There is a proportional relation between crime figures and level of education” or “There is an inversely proportional relation between income and level of education”. We wanted to see which opinions students had before they started working with the data and if these opinions were influenced after they had analyzed the data. A group of 40 students went to work with the data. The group was divided into 20 students who went to work with real data and 20 went to work with ‘fake’ data. Students were emailed with the three data files and the following assignment: “check CSV (Excel) file in the attachment. Please try this to do an analysis. Try to draw a minimum of 1, a maximum of 2 conclusions from it… this can be anything. As long as it leads to a certain conclusion based on the figures.” In addition, there was also a survey in which we tried to find out how students currently think about correlations between crime, income and educational level. Additionally, some students were interviewed to get some insights into the figures collected by the survey.  

Results

For the survey, 40 students have been approached. The response consisted of 25 students. All students indicated that working with real data is more fun, challenging and concrete. It motivates them. Students who worked with fake data did not like this as much. In interviews they indicated that they prefer, for example, to work with cases from companies rather than cases invented by teachers. In the interviews, the majority of students indicated that by working with real data they have come to a different understanding of crime and the reasons for it. They became aware of the social impact of data and they were triggered to think about social problems. To illustrate, here some responses students gave in interviews “Before I started working with the data, I had always thought that there was more crime in districts with a low income and less crime in districts with a high income. After I have analyzed the data, I have seen that this is not immediately the case. So my thought about this has indeed changed. It is possible, but it does not necessarily have to be that way.” (M. K.) “At first, I also thought that there would be more crime in communities with more people with a lower level of education than in communities with more people with a higher level of education. In my opinion, this image has changed in part. I do not think that a high or low level of education is necessarily linked to this, but rather to the situation in which they find themselves. So if you are highly educated, but things are really not going well (no job, poor conditions at home), then the chance of criminality is greater than if someone with a low level of education has a job.” ( A. K.) “I think it has a lot of influence. You have an image and an opinion beforehand. But the real data either shows the opposite or not. And then you think, “Oh yes, this is it.’. And working with fake data, is not my thing. It has to provide real insights.” (M.D.)

Conclusion

Our experiment provided positive indications that contributing to the Bildung component of education by using open data in data analysis exercises is possible. Next steps to develop are both extending these experiences to larger groups of students and to more topics in the curriculum.  

References

Atenas, J. & Havemann, L. (2015). Open Data as Open Educational Resources: Towards Transversal Skills and Global Citizenship. Open praxis7(4), 377-389. http://dx.doi.org/10.5944/openpraxis.7.4.233 Atenas, J., & Havemann, L. (Eds.). (2015). Open Data as Open Educational Resources: Case studies of emerging practice. London: Open Knowledge, Open Education Working Group. https://education.okfn.org/handbooks/open-data-as-open-educational-resources/ 
About the authors  Erdinç Saçan is a Senior Teacher of ICT & Business and the Coordinator of the Minor Digital Marketing @ Fontys University of Applied Sciences, School of ICT in Eindhoven, the Netherlands. He previously worked at Corendon, TradeDoubler and Prijsvrij.nl       Robert Schuwer is Professor Open Educational Resources at Fontys University of Applied Sciences, School of ICT in Eindhoven, the Netherlands and  holds the UNESCO Chair on Open Educational Resources and Their Adoption by Teachers, Learners and Institutions.

Learning Analytics Policy Development

- June 25, 2018 in communication, Data, Featured, guestpost

Written by Anne-Marie Scott  — The University of Edinburgh has just launched their Principles and Purposes for Learning Analytics. In order to develop institutional policy on learning analytics, in 2016 we convened a task group reporting to our Senate Learning and Teaching Committee, and our Knowledge Strategy Committee. The task group was convened by Professor Dragan Gasevic, Chair of Learning Analytics and Informatics. The group included Professor Sian Bayne, Assistant Principal Digital Education; representatives from academic Colleges; the Edinburgh University’s Students Association; and representatives from Student Systems and Information Services. Our Director of Academic Services produced an initial draft of a Learning Analytics policy for review by our institutional task group. It was a relatively detailed policy which covered the following sorts of topics:
  • Definitions
  • Sources of data for learning analytics
  • Sources of data for learning analytics
  • Initiating learning analytics activities
  • Transparency and consent
  • Privacy and access to data
  • Retention and disposal of data
  • Validity and interpretation of data
  • Supporting positive interventions
  • Enabling students to reflect on their learning
  • Supporting staff to make the most of learning analytics
  • Oversight of Learning Analytics activities
  • Other relevant policies
Ethical values, legal obligations and the reasons for engaging with learning analytics were all embedded in the policy, but as we worked on revisions, considered inputs from external sources, and planned how to consult on a draft it became clear that this detailed policy was likely to beg more questions than it answered without being more explicit about our values and our ethical position upfront. We also had to contend with periods of time where there was limited data protection resource available to the task group, and where the legal basis for processing under GDPR that would be available to us was still being debated in the House of Lords. At the same time as we were developing local policy, colleagues at Edinburgh (Prof Dragan Gasevic and Dr Yi-Shan Tsai) were involved with the EU Sheila project, developing a learning analytics policy development framework for the EU. There were several key outputs from that project that we used in pre-print form to inform our work: In particular, the group concept mapping activity carried out by the Sheila project (surveying various European Universities) identified that defining objectives for learning analytics was very important, but also very hard (http://sheilaproject.eu/wp-content/uploads/2017/04/The-state-of-learning-analytics-in-Europe.pdf). As part of our local policy development, myself and Dragan Gasevic met and discussed what we felt were the 6 main purposes for learning analytics in an Edinburgh context, and these were written up into the policy as a means of tackling this issue head-on for Edinburgh. The literature review on learning analytics adoption that the Sheila project produced also identified various challenges to adoption, and on further consideration I drafted a separate Purposes and Principles document which extracted various of the principles embedded in the detailed policy and responded to many of the challenges and concerns identified in the literature review. Given some of the challenges we were experiencing around clarity on new data protection legislation for resolving areas the more detailed policy, this was the point at which our task group decided to separate the two pieces and start with a consultation on Purposes and Principles only. The Purposes and Principles were outlined and discussed at Senate in early 2017 and then taken to each School for discussion as part of the consultation plan that Academic Services devised for us. To support this consultation we also developed a webpage that outlined existing research and operational activities in learning analytics at Edinburgh (https://www.ed.ac.uk/information-services/learning-technology/learning-analytics). This high-level values-first route proved to be an effective way to start, as consultation with many Schools identified that the level of knowledge and understanding of learning analytics was highly variable across the institution, and that there were significant pockets of concern about ethics and about support for staff and students to make more use of data. The Sheila project also ran a student survey at Edinburgh during this time period and we were also able to finesse the Principles and Purposes to respond to student concerns and expectations. In considering how to achieve oversight and governance in the absence of the more detailed policy, and in a potentially quite complex and changing area, we also proposed the establishment of a Learning Analytics Review Group. As we pursue more data-driven operational activities this helps close out an ethical review gap in our operational activities. This governance model is now of interest to colleagues working on institutional data governance activities more generally. Once the Principles and Purposes were approved, with support from our Data Protection Officer, and more clarity on GDPR we were then able to tidy up the more detailed policy which defines the ‘mechanics’ of how activities can be initiated, what roles and responsibilities exist, what sources of data might be implicated etc. This policy was approved by our Senate Learning and Teaching Committee in May 2018. Importantly, this policy has also been able to link in to other work around data governance within the institution, and formally recognises the role that our institutional ‘Data Stewards’ have to play in the approvals process for learning analytics projects. Important inputs to the development of policy (as well as the Sheila project inputs) included:   — About the author
Anne-Marie Scott is Deputy Director of Learning, Teaching and Web Services, at the University of Edinburgh. Her background is in the design, management and support for academic IT services, particularly those used to support teaching and learning activities online. Amongst her interests are the use of new media and the open web in teaching and learning, scalable online learning platforms, and learning analytics.   Originally published in https://ammienoot.com/brain-fluff/learning-analytics-policy-development/ 

Learning Analytics Policy Development

- June 25, 2018 in communication, Data, Featured, guestpost

Written by Anne-Marie Scott  — The University of Edinburgh has just launched their Principles and Purposes for Learning Analytics. In order to develop institutional policy on learning analytics, in 2016 we convened a task group reporting to our Senate Learning and Teaching Committee, and our Knowledge Strategy Committee. The task group was convened by Professor Dragan Gasevic, Chair of Learning Analytics and Informatics. The group included Professor Sian Bayne, Assistant Principal Digital Education; representatives from academic Colleges; the Edinburgh University’s Students Association; and representatives from Student Systems and Information Services. Our Director of Academic Services produced an initial draft of a Learning Analytics policy for review by our institutional task group. It was a relatively detailed policy which covered the following sorts of topics:
  • Definitions
  • Sources of data for learning analytics
  • Sources of data for learning analytics
  • Initiating learning analytics activities
  • Transparency and consent
  • Privacy and access to data
  • Retention and disposal of data
  • Validity and interpretation of data
  • Supporting positive interventions
  • Enabling students to reflect on their learning
  • Supporting staff to make the most of learning analytics
  • Oversight of Learning Analytics activities
  • Other relevant policies
Ethical values, legal obligations and the reasons for engaging with learning analytics were all embedded in the policy, but as we worked on revisions, considered inputs from external sources, and planned how to consult on a draft it became clear that this detailed policy was likely to beg more questions than it answered without being more explicit about our values and our ethical position upfront. We also had to contend with periods of time where there was limited data protection resource available to the task group, and where the legal basis for processing under GDPR that would be available to us was still being debated in the House of Lords. At the same time as we were developing local policy, colleagues at Edinburgh (Prof Dragan Gasevic and Dr Yi-Shan Tsai) were involved with the EU Sheila project, developing a learning analytics policy development framework for the EU. There were several key outputs from that project that we used in pre-print form to inform our work: In particular, the group concept mapping activity carried out by the Sheila project (surveying various European Universities) identified that defining objectives for learning analytics was very important, but also very hard (http://sheilaproject.eu/wp-content/uploads/2017/04/The-state-of-learning-analytics-in-Europe.pdf). As part of our local policy development, myself and Dragan Gasevic met and discussed what we felt were the 6 main purposes for learning analytics in an Edinburgh context, and these were written up into the policy as a means of tackling this issue head-on for Edinburgh. The literature review on learning analytics adoption that the Sheila project produced also identified various challenges to adoption, and on further consideration I drafted a separate Purposes and Principles document which extracted various of the principles embedded in the detailed policy and responded to many of the challenges and concerns identified in the literature review. Given some of the challenges we were experiencing around clarity on new data protection legislation for resolving areas the more detailed policy, this was the point at which our task group decided to separate the two pieces and start with a consultation on Purposes and Principles only. The Purposes and Principles were outlined and discussed at Senate in early 2017 and then taken to each School for discussion as part of the consultation plan that Academic Services devised for us. To support this consultation we also developed a webpage that outlined existing research and operational activities in learning analytics at Edinburgh (https://www.ed.ac.uk/information-services/learning-technology/learning-analytics). This high-level values-first route proved to be an effective way to start, as consultation with many Schools identified that the level of knowledge and understanding of learning analytics was highly variable across the institution, and that there were significant pockets of concern about ethics and about support for staff and students to make more use of data. The Sheila project also ran a student survey at Edinburgh during this time period and we were also able to finesse the Principles and Purposes to respond to student concerns and expectations. In considering how to achieve oversight and governance in the absence of the more detailed policy, and in a potentially quite complex and changing area, we also proposed the establishment of a Learning Analytics Review Group. As we pursue more data-driven operational activities this helps close out an ethical review gap in our operational activities. This governance model is now of interest to colleagues working on institutional data governance activities more generally. Once the Principles and Purposes were approved, with support from our Data Protection Officer, and more clarity on GDPR we were then able to tidy up the more detailed policy which defines the ‘mechanics’ of how activities can be initiated, what roles and responsibilities exist, what sources of data might be implicated etc. This policy was approved by our Senate Learning and Teaching Committee in May 2018. Importantly, this policy has also been able to link in to other work around data governance within the institution, and formally recognises the role that our institutional ‘Data Stewards’ have to play in the approvals process for learning analytics projects. Important inputs to the development of policy (as well as the Sheila project inputs) included:   — About the author
Anne-Marie Scott is Deputy Director of Learning, Teaching and Web Services, at the University of Edinburgh. Her background is in the design, management and support for academic IT services, particularly those used to support teaching and learning activities online. Amongst her interests are the use of new media and the open web in teaching and learning, scalable online learning platforms, and learning analytics.   Originally published in https://ammienoot.com/brain-fluff/learning-analytics-policy-development/ 

Eduskunnan rajapinta valtiopäiväasiakirjoihin on nyt auki

- May 2, 2018 in api, arvot, avoin data, avoin demokratia, blog, Data, demokratia, Featured, kansalaiset, Open Democracy, osallistuminen, päätösdata, parlamentti, rajapinta, tieto, tietojohtaminen

Täysistuntojen pöytäkirjat, kansanedustajien puheenvuorot ja valiokuntien asiantuntijalausunnot ovat nyt tarjolla vapaasti käytettävänä koneluettavassa muodossa. Data on saatavilla osoitteessa avoindata.eduskunta.fi. Aineisto tarjoaa mahdollisuuksia erityisesti sovelluskehittäjille, tutkijoille ja datajournalisteille. Avatut tiedot ovat tältä vaadikaudelta, pääosin alkaen vuodesta 2015. Äänestystietoja on tarjolla jo 1990-luvulta lähtien. Meneillään on myös toinen hanke, jossa muokataan vuosien 1907-2000 aineistoja avattavaan muotoon. Hankkeen odotetaan julkaisevan tuloksia jo tämän vuoden aikana. Kansanedustajien henkilötiedot jätettiin pois rajapintaan nyt avattavista aineistoista koska se sisältää mm. kansanedustajien sukulaissuhteita kuvaavia tietoja. Asiaan vaikuttaa EU:n uuden tietosuojakäytännön kannalta vielä eduskunnassa ratkaisemattomia linjavetoja. Poissaolotiedot ovat mukana, mutta pdf-asiakirjoina. Pääosin data on rakenteisessa JSON-muodossa. Aineiston käyttäminen edellyttää sitoutumista eduskunnan määrittelemiin käyttöehtoihin. Data on lisensoitu Creative Commons Nimeä 4.0 -lisenssillä, mikä tarkoittaa, että aineistoa saa käyttää vapaasti mihin tahansa käyttötarkoitukseen, muokata ja edelleen julkaista muokattuja tuotoksia, mutta aineiston lähteeseen pitää viitata. Palaset liikahtavat kohdilleen vähitellen Eduskunnan datan avaus on pitkän prosessin tulos. Eduskuntatiedotus lienee avannut keskustelun avoimen datan yhteisön kanssa ensimmäisen kerran jo vuoden 2010 Nettiajan kansalaisyhteiskunta -verkoston tapahtumassa. Kun avoimen datan ilmiö herätti vilkkainta keskustelua vuosikymmenen alussa, eduskunta ei juossut trendin perässä, vaan oli lähdössä uusimaan kokonaan keskeisiä tietojärjestelmiään. Ja kuten joskus tapana on, projektit venyvät. Eduksi/Vaski-nimellä tunnetun hankkeen myötä uuden järjestelmän käyttöönotto venyi vuoteen 2015 saakka. Tuotos näkyi yleisölle muun muassa uusien verkkosivujen muodossa, mutta toive rajapinnasta ei konkretisoitunut. Vuoden 2016 alussa eduskunnan kirjasto kutsui kehittäjät avoimen datan ja avoimen demokratian tilaisuuteen, jossa eri toimijat jakoivat kokemuksiaan päätösdatan avaamisesta ja hyödyntämisestä. Tilaisuuden varsinainen anti oli eduskunnan avoimen datan testirajapinnan julkistaminen. Rajapinnan kautta sai määräajan kyseltyä varsinaista tuotantojärjestelmän dataa, mutta yhteisön palaute rajapinnan toteutuksesta oli kriittinen. Testirajapinta tarjoili datan muodossa, joka herätti jatkokäytön kannalta enemmän kysymyksiä kuin antoi vastauksia.  

Kansalaiset voivat valvoa edustajien työskentelyä niin yleisölehteriltä kuin rajapinnan kautta. Molemmat mahdollisuudet vaativat pienen kynnyksen ylittämistä, mutta ovat avoinna kaikille ja palvelevat erilaisia tarpeita.

Eduskunnan projektiryhmän ja sen IT-toimittajan, GoForen keskusteluissa huomattiin seuraavaksi, että rajapinnan ensimmäinen versio oli suunniteltu väärien periaatteiden mukaan. Se oli liian suoraviivaisesti kiinni tuotantojärjestelmissä. Toteutus piti suunnitella uusiksi. Oman haasteensa tehtävään toi, ettei eduskunnalla ole yhtä datavarastoa, jonka päälle uusi rajapinta rakennetaan, vaan ainesto kootaan useista eri järjestelmistä ja niiden välillä liikkuvasta tiedosta. Lisäksi uudesta rajapinnasta päätettiin tehdä eduskunnan ensimmäinen pilvipalvelu, jonka hyväksyttäminen talon johdossa oli oma asiansa. Nyt keväällä 2018 eduskunnan kirjasto kutsui kehittäjäyhteisön tilaisuuteen, jossa uusi viittä vaille valmis rajapinta esiteltiin. Se on esitelty jo talon sisällä, ja avoin yleisötilaisuus on suunnitteilla vielä ennen kesää, kun projekti saadaan lopullisesti päätökseensä viimeisten viilausten myötä. Eduskunta liittyy Suomessa vielä harvalukuiseen julkisten toimijoiden joukkoon, jotka tarjoavat avointa päätöksentekodataa asianhallintajärjestelmistään. Helsingin kaupunki on toiminut edelläkävijänä Open Ahjo -rajapinnan lanseeramisella jo 2013. Kuuden suurimman kaupungin 6aika-strategia kehitti yhteisen, Helsingin mallia ja Popolo-standardia mukailevan rajapinnan, jota testikäytettiin Tampereen, Vantaan ja Oulun aineistoilla. Kansainvälisesti avointa dataa tarjoilevien parlamenttien määrä alkaa lähennellä kahtakymmentä. Vain mielikuvitus ja tietotekniset taidot ovat rajana Käyttötarkoituksesta riippumatta vapaasti käytettävä (lakien noudattamisesta rajapinta ei vapauta) data voi olla erityinen aarre politiikka-tekno-nörteille. Niin Suomessa kun maailmalla on kehitetty niin kutsuttuja parlamentaarisen valvonnan verkkopalveluita, jotka yksinkertaistavat ja nostavat asiakirjoista esiin vallankäyttäjien valvonnan kannalta olennaisia tietoja. Monimutkaiseksi tulkinnan vain tekee sen, että hallitus-oppositioasetelman ja eduskunnan kokouskäytäntöjen takia yksittäisessä äänestyksessä esimerkiksi ympäristölakipakettia vastaan äänestäminen ei suoraviivaisesti tarkoita, etteikö kansanedustaja pitäisi vihreitä arvoja tärkeinä, sillä voi olla että edustajan oma puoli ajaa vielä kunnianhimoisempia ympäristötavoitteita, jotka ovat käsittelyssä vasta seuraavassa äänestyksessä. Esimerkiksi Kansan muisti -palvelu on ollut ensimmäisiä internet-ajan vallan vahtikoirapalveluita Suomessa. Aivan viime vuosina palvelu on ollut teknisellä tauolla, mutta nyt tekemässä paluuta datan tultua saataville ensimmäistä kertaa helposti jatkokäytettävässä muodossa. Toisena esimerkkinä on Democratize-niminen sovellus, joka antaa käyttäjilleen mahdollisuuden äänestää itse eduskunnan juuri käsittelemistä asioista. Kansan muisti ja Democratize ovat tyyppiesimerkkejä päätösdataa hyödyntävistä sovelluksista, joista monet joko tarjoavat mahdollisuuksia edustajien valvontaan tai omaan henkilökohtaiseen enemmän tai vähemmän näennäiseen osallistumiseen. Lisäksi datajournalistit ovat visualisoineet kansanedustajien ja puolueiden arvoja tarkastellessaan miten päättäjien arvot jakautuvat eri asioissa, ja kuinka jakolinjat muuttuvat verrattuna perinteisiin asetelmiin. Datajournalismista on lyhyt matka tutkimukseen, ja esimerkiksi politiikan tutkimuksessa on alettu viime aikoina hyödyntää laskennallisia menetelmiä. Yhdistettynä sosiaalisen median ja vaalikoneiden aineistoihin päätöksenteko-aineistot voivat tarjota mahdollisuuksia arvioida niin yksittäisten edustajien, puolueiden kuin hallituksen tai opposition tavoitteiden aikaansaamista kuin päätösten vastaavutta julkiseen keskusteluun tai kansalaisten arvoihin nähden. The post Eduskunnan rajapinta valtiopäiväasiakirjoihin on nyt auki appeared first on Open Knowledge Finland.

Illuminating the global OER community with data

- January 29, 2018 in communication, Data, Featured, guestpost, oer, world

This is the first post of a serie of notes shared by the members of the Open Education Working Group Advisory Board. In this post, Jan Neumann (@trugwaldsaenger ‏) shares the latest news of the OER World Map project — The goal of the OER World Map project is to illuminate the global OER community with meaningful data. It is a structured educational network, which provides a unique identifier for each building block of the OER ecosystem, allowing educational professionals from different disciplines to share their knowledge with hitherto unknown precision and reliability. Our current focus lies on three main user stories: Connecting OER actors with another, identifying OER sources and providing statistics on OER and Open Education. The underlying data set is extremely flexible and there are so many use cases for it, that it can facilitate interaction and collaboration by scaffolding a wide range of data led activities. Since being funded by the William and Flora Hewlett Foundation in 2015, we have solved many practical challenges. Due to the expansive and generalised scope of the project as well as its high complexity we needed time for cautious approaching the right technical and organizational solutions required by the global OER community. Last year we claimed to have reached adolescence in the sense that the project started to provide value for the community. Now we are happy, that our maturity level proceeded so that full adulthood will be reached in the course of the year. Nevertheless this does not mean that all problems have been solved and the work is done. Rather it means that the platform has evolved so much, that it is now ready to be adopted by the global Open Education Community with significantly increased intensity. The good news is, that we believe to have proven that centralized data collection makes sense and can be done with reasonable effort. Lately we engaged strongly in supporting the current German OER funding line. For this, we adopted the platform, so that it can model programs and created a country map which shows only regional entries. The country map was integrated in OERinfo – a recently launched site which aims at providing quality information needed to mainstream OER. We also participated in the creation of a UNESCO-Report and the OER Atlas 2017 which are both characterized by the inclusion of quantitative data received from the OER World Map. We believe that many of the lessons learned can be transferred to other countries. Especially we believe that country maps will be a reasonable way to address local communities and we hope that many maps for other countries will follow soon! Also we learned, that effective data collection works best, when being driven by a professional editor in cooperation with the local OER community. From this point of view we believe, that the OER World Map provides best results when “top-down” and “bottom-up” elements are combined. At the same time we are continuously improving and expanding our platform: A German translation was provided, a Brazilian Portuguese version is on its way. In addition, our new functionality to set lighthouses and likes as well as the inclusion of OER awards will support users to find high quality initiatives and good practice examples more easily. Last – but by no means least – we are happy to announce that we launched our new landing page just some days ago! And there are several exciting developments in the pipeline for 2018. Currently we are working on finishing the refactoring of our complete frontend, which will significantly improve the performance of the system as well as its usability. The inclusion of subscriptions and notifications will provide users with regular updates of information relevant for them. Another major milestone will be the inclusion of subcategories for all data types, which will bring browsing and searching to a greater level of granularity. So what can you do?
  • If you have not done so yet, please register on the map and show that you share our vision of connecting the global OER community.
  • Please make also sure, that your initiative is on the map and share your lessons learned as a story.
  • For research institutes, government agencies or libraries it can be interesting to host a country map.
If you are interested in learning more, please have a look at our latest presentation. We would love to learn what function you would like to see on the World Map. If you do have any ideas, questions or comments, please contact us (info@oerwordmap.org).   — About the author Jan L. Neumann is part of our advisory board and is working as Head of Legal Affairs and Organization at the North Rhine-Westphalian Library Service Centre (hbz) in Cologne, Germany. He studied law, economy and systems thinking and has more than 15 years of experience within international project management for different publishing houses and libraries. He is a member of the Education Expert Committee of the German Commission for UNESCO and blogs about Open Educational Resources (OER) on OERSYS.org. Since 2013 he manages the OER World Map project, which is funded by the William and Flora Hewlett Foundation and aims at providing the most complete and comprehensible picture of the global Open Educational Resources (OER) movement so far. Jan is a frequent speaker at OER conferences and participated in the organization of OERde 14, OERde 15 and OERde 16 Festival. Nevertheless he considers himself a non-expert in OER to stress that having the courage to think by yourself is one important aspect of the empowerment which comes along with open education. He can be followed as @trugwaldsaenger on Twitter.

Illuminating the global OER community with data

- January 29, 2018 in communication, Data, Featured, guestpost, oer, world

This is the first post of a serie of notes shared by the members of the Open Education Working Group Advisory Board. In this post, Jan Neumann (@trugwaldsaenger ‏) shares the latest news of the OER World Map project

OpenCon Santiago 2017: No more streaks in the water

- January 4, 2018 in #opencon, Data, Events, Featured, guestpost, oer, Open Data, Open Science, open-education, world

Guest post by Ricardo Hartley @ametodico and Carolina Gainza @cgainza

When organizing any event, questions always arise; Will enough people come? Do those who have positions to make the changes come? Will come those who should have interest …

Paljon on avattu dataa, vaan mistä tehdään rahaa?

- April 27, 2017 in 6aika, avoimuus, avoin, avoin data, blog, businss, Data, Featured, liiketoiminta, okffi, okfi, Open Knowledge, open knowledge finland, tieto

Tieto on aarre. Aarrekartta tiedon luokse.

Avoimen datan kartoituksessa etsitään tiedosta tehtyjä aarteita. (Kuva on CC-lisensoitua kuvapankkimateriaalia, eikä sen tekemisessä ole käytetty julkisia varoja)

Viitisen vuotta sitten Suomessa oli valloillaan todellinen avoimen datan hype. Uudet paremmat palvelut olivat tiedon vapauttamisen puolestapuhujien ensisijainen myyntiargumentti. Julkinen hallinto tarttui myös toimeen ja asian edistämiseen on sijoitettu ainakin seitsemännumeroisia summia. Suurimmat kaupungit, yliopistot ja monet muut julkiset organisaatiot ovat avanneet tietojaan vapaaseen käyttöön. Mutta ovatko yritykset ehtineet jo hyödyntää uusia aineistoja liiketoiminnassaan?   Open Knowledge Finland kartoittaa avoimen datan käyttöä liiketoiminnassa 6Aika-strategian Avoin data ja rajapinnat -kärkihankkeen toimeksiannosta kevään aikana. Parhaat esimerkit erilaisista palveluista, innovaatioista, sovelluksista ja yrityksistä tullaan julkaisemaan databusiness.fi -sivustolla toukokuussa. Hyviä kokemuksia haetaan niin kotimaasta kuin rajojen ulkopuolelta. Projektissa työskentelevät projektipäällikkö Raimo Muurinen sekä asiantuntijat Kari A. Hintikka ja Mikael Seppälä. OKFI:n toiminnanjohtaja Teemu Ropponen avustaa myös projektia.   “Sellaiset palvelut ja käyttötavat kiinnostavat, joita voisi soveltaa Suomessa, nyt tai tulevaisuudessa” sanoo kärkihankkeen vetäjä Matti Saastamoinen. “Pyrimme tarjoamaan näyttöä hyvistä mahdollisuuksista sekä datan hyödyntäjille että tuottajille”   Avoimen datan liiketoimintalupauksen toteutumisen perään on jo ehditty kysellä. Esimerkiksi Ville Peltola kirjoitti pari vuotta sitten, että alalla on suhteellisen vähän startupeja, koska julkinen hallinto on avannut lähinnä staattisia datasettejä jotka eivät kiinnosta kehittäjiä. Tulevaisuus näyttää hänen mukaansa kuitenkin paremmalta, kun jatkuvasti päivittyviä datavirtoja tulee enemmän tarjolle.   Peltolankin esiinnostama liikennedata on edelleen hyvä esimerkki. Liikennevirasto ja HSL tekevät parhaillaan pohjatyötä alan ekosysteemin mahdollistamiseksi avoimien rajapintojen ja avoimen lähdekoodin avulla. Otsikoissa onkin näkynyt erilaisia avauksia liikkuminen palveluna -liiketoiminnan ensi askeleista.   Vielä laajempaa yleisöä koskettaa julkinen päätöksenteko, johon liittyviä aineistoja on avautunut ja avautumassa lähiaikoina. Avoin data ja rajapinnat -hanke on työskennellyt Suomen suurimpien kaupunkien päätösdatan avaamiseksi Helsingin pioneerityön pohjalta. Ensi alkuun on julkaistu päätöstietoja rakenteisessa muodossa Oulusta ja Vantaalta, sen jälkeen seuraavat muut 6Aika-kaupungit.   Kaupunkien lisäksi valtiotason päätökset ovat jo osittain avattu. Eduskunta on saanut oman rajapintansa rajoitettuun koekäyttöön pitkän odotuksen jälkeen. Lakikokoelma Finlex tuli tarjolle linkitettynä avoimena datana Aalto-yliopiston tutkijoiden ansiosta viime vuonna.   Kartoituksessa tarkastellaan myös avoimen datan liiketoiminnan ansaintamalleja. Pohjana hyödynnetään Matti Rossin tutkimukseen pohjautuvaa viiden ansaintamallin jaottelua. Sen mukaan yksi yleinen liiketoimintamalli on datan poiminta ja paketointi uudelleenkäyttö- ja myyntikuntoon muutetussa muodossa.   Menestyksekkäiden businesscasejen lisäksi huomioidaan yhteiskunnallisesti merkittävät tavat hyödyntää avointa dataa. Avoimen datan myyntipuheeseen ovat nimittäin kuuluneet säästöt hallinnon tehostumisen ansiosta sekä demokratian vahvistuminen.   Paljonko rahaa avoin data on sitten tuonut tai säästänyt? Se on valitettavasti liian suuri kysymys selvitettäväksi tässä yhteydessä, vaikka tietysti sitäkin kiinnostavampi. Ennen kesälomia tiedämme kuitenkin minkälaista liiketoimintaa avoimella datalla tehdään jo nykysin, ja mitkä aineistot ovat osoittautuneet käyttökelpoisimmiksi. Samalla voi saada vinkkiä siitä, mitä kannattaisi avata seuraavaksi. Showcaseja esitellään Mitä data mahdollistaa matkailualalla -tapahtumassa Allas Sea Poolilla Helsingissä 23.5.2017. The post Paljon on avattu dataa, vaan mistä tehdään rahaa? appeared first on Open Knowledge Finland.

How students can help EU policies work better thanks to open data and civic technology

- November 30, 2016 in Data, Featured, guestpost, higher education, italy, oer, Open Data, open-education

Post written by Luigi Reggi 

Three small but important steps toward a more participatory EU policy were made in the last few weeks between Brussels and Rome, Italy. They are three episodes of a series of productive encounters between students equipped with open data and civic technology and policy makers managing EU funding.

Civic monitoring of EU funding as a way to assess results

The first episode happened  in Brussels. On November 22, a group of Italian higher education students engaged in a productive discussion with the European Commission – DG Regional and Urban Policy and the EU Committee of the Regions. The debate was focused on the role of open data and public participation to assess the results of the European Cohesion Policy from the point of view of the final beneficiaries. The team MoniTOreali – composed of students from the University of Turin and led by Alba Garavet, responsible for Turin’s  Europe Direct Centre – had the chance to present the results of an intense “civic monitoring” activity focused on one of the most visible EU-funded projects in the city. Its goal is the renovation of the “Giardini Reali”, the historical gardens of Turin’s Royal Palace, one of the city’s landmarks.  With a total funding of less than 2 million euros, the project is hardly one of biggest investments of EU policy in Italy. However, its central position in the urban landscape gives it the potential to shape the way citizens perceive the contribution of the European institutions to the improvement of their neighborhoods. The goal of this monitoring was to find out how the EU money was spent and whether the project delivered the promise or not.
The Royal Gardens in Turin, Italy, funded by European Structural Funds. Photo: MoniTOreali

The Royal Gardens in Turin, Italy, funded by European Structural Funds. Photo: MoniTOreali

What MoniTOreali students found was mixed results. While the project should have been completed by 2012, actually it is still under way due to a series of administrative delays. Its implementation is also influenced by a complex social environment, as conflicting social groups have different views on the future of the gardens and this had the effect of stalling policy decisions. To disentangle this intricate web of relations, the students interviewed experts, citizens and local public administrators. They analyzed the project’s objectives, strengths, weaknesses, history and recent developments in a civic monitoring report, which was published in the independent civic technology platform Monithon, the “Monitoring Marathon” of the European funding in Italy. The students also provided suggestions and ideas on how solve some the project’s issues. But the most interesting aspect of this experience is that Mrs Garavet succeeded in adapting the methodology of A Scuola di OpenCoesione (ASOC) – which was originally created by the Italian Government for high school students – to a higher education course.  She was able to effectively combine her experience as an activist in the Monithon Piemonte civic community with the more formal, six-step ASOC methodology, which also includes sessions on open data, data journalism, EU funding, and field research.  Earlier this year, Chiara Ciociola, the ASOC project manager, actively participated in the teaching activities in Turin to promote a sort of cross-fertilization between the two communities.  More information on the ASOC method and results is included in the book edited by Javiera Atenas and Leo Havemann. The idea is that an improved version of the course’s syllabus could be adopted and used by other universities in Italy and in Europe to replicate the same practice, contextualising its application. The fact that all European Countries share the same rules when it comes to EU funding can help spread a common approach. It turned out that EU officials loved the idea. The main conclusion of the meeting was that participation in the civic monitoring of EU policy could be a way to bridge the gap between EU institutions and the public. Moreover, the spread of these activities across the EU could also help policymakers evaluate the outcome of interventions from the point of view of the local communities. This is particularly important given that, according to recent developments, EU policies will be more and more focused on actual results in terms of real change for the final beneficiaries. More concretely, the European Commission proposed to use its programme “REGIO P2P” to fund an exchange of civic monitoring practices between EU authorities managing the funds in different Countries.

A new way to communicate policy outputs

The second episode was a stimulating workshop organized by the EU official Tony Lockett at the European Conference on Public Communication. As Lockett describes very well in this report, open data initiatives such as the EU Portal or the DG Regional Policy open data website are probably not enough to get real impact if not combined with effective citizen participation. In particular, Simona De Luca – representing the OpenCoesione team at the Italian government – showed how independent civic monitoring of EU-funded projects, based on the open data published on the governmental portal, can profoundly change the way the policy is communicated to the public.  While most of the “good stories” about EU funding are selected by a few experts at the managing authorities and then told by communication officers, the idea of relying on real stories by citizens for other citizens makes official communication extraordinarily powerful. People’s stories, based on official data but augmented thanks to new information collected with a sound and shared methodology, can represent not only a potential risk for the government – when the projects don’t match the expectations – but also a great way to show how problems can be solved together thanks to a meaningful collaboration between governments and citizens.  
Source: OpenCoesione - The Italian open government strategy on cohesion policy

Source: OpenCoesione – The Italian open government strategy on cohesion policy

The third episode happened last week at the Italian annual meeting with the European Commission on EU Cohesion Policy. The Agency for Cohesion, a national administration responsible for monitoring the implementation of EU Cohesion policy in Italy, for the first time used the stories from the citizens to present the results of EU Structural Funds. In particular, a set of good practices from the 2007-13 period was selected based on the civic monitoring reports included in the Monithon platform.  Most of the projects presented were monitored by the A Scuola di OpenCoesione high school students in different locations. The only exception was a project in Ancona, which was the focus of Action Aid’s School of participation. Although problematic projects were not mentioned at all during the event, the presentation was the first attempt in Italy to represent the results of EU Policy “from the point of view of the citizens”.  A kind of Copernican revolution for official communication that surprised most of the participants.
Current civic monitoring reports as displayed on Monithon.it

Current civic monitoring reports as displayed on Monithon.it

Collaborating with the Open Government ecosystem

These three examples indicate that a process of positive change is under way among European and national administrations that manage EU funds toward a more collaborative management of EU policy.  However, stronger and more stable mechanisms are needed to ensure real participation in the monitoring and evaluation of EU policies. What seems to drive this change is not only the desire for a more open and inclusive public policy, but also the urgent problem of finding out whether the projects funded really deliver or not. It is in the interest of all actors involved to assess the actual performance of the huge amount of money that flows from the EU budget to the European regions and cities, given the common ambitious goals of sustainable growth, innovation, job creation, social inclusion, and education. I believe that this question cannot be answered only with aggregated figures or econometric exercises. It requires a painstaking, bottom-up assessment of each single project involving local communities, journalists, analysts, and public officials at the EU, national and regional levels. This is a complex task that public authorities cannot handle by themselves. They need to be ready and capable to collaborate with the whole open government ecosystem composed in this case of
  • open data producers such as OpenCoesione.gov.it
  • government proactive initiatives such as A Scuola di OpenCoesione, which focus on the crucial element of civic learning
  • data users like the MoniTOreali group developing the right skills and expertise to provide meaningful feedback
  • civic tech initiatives like Monithon
  • intermediaries such as local media or NGOs aggregating and interpreting the feedback from the final beneficiaries
  • policy makers willing to listen and act upon the suggestions from the public.
Monithon calls it a “monitoring marathon”, indeed. If you want to know more about the open government ecosystem of the EU Cohesion Policy in Italy you can read this paper, which develops a conceptual model based on this case.BIO screen-shot-2016-11-30-at-17-02-16Luigi Reggi is a technology policy analyst at the Italian government and a PhD student in Public Administration and Policy at the State University of New York at Albany, USA. He is interested in Open Government Data, collaborative governance and European Cohesion Policy